Aabhas Sharma

schoolIBMbar_chart#2595 Global Rank

Total Papers

HCI Rank

#2595

Research Areas

Years Active

Recent Publications

Reliance and Automation for Human-AI Collaborative Data Labeling Conflict Resolution

Human data labeling with multiple labelers and the resulting conflict resolution remains the norm for many enterprise machine learning pipelines. Conflict resolution can be a time-intensive and costly process. Our goal was to study how human-AI collaboration can improve conflict resolution, by enabling users to automa…

CSCW '213rd AuthorData Work and AI

AI-Assisted Human Labeling: Batching for Efficiency without Overreliance

Human labeling of training data is often a time-consuming, expensive part of machine learning. In this paper, we study "batch labeling", an AI-assisted UX paradigm, that aids data labelers by allowing a single labeling action to apply to multiple records. We ran a large scale study on Mechanical Turk with 156 particip…

IUI '215th+ AuthorExplainable AI (XAI) +2

Increasing the Speed and Accuracy of Data Labeling Through an AI Assisted Interface

Labeling data is an important step in the supervised machine learning lifecycle. It is a laborious human activity comprised of repeated decision making: the human labeler decides which of several potential labels to apply to each example. Prior work has shown that providing AI assistance can improve the accuracy of bi…

IUI '214th AuthorExplainable AI (XAI) +2

The Design and Development of a Game to Study Backdoor Poisoning Attacks: The Backdoor Game

Recently, AI Security researchers have identified a new way crowdsourced data can be intentionally compromised. Backdoor attacks are a process through which an adversary creates a vulnerability in a machine learning model by “poisoning’' the training set by selectively mislabelling images containing a backdoor object.…

CHI '215th+ AuthorComputational Methods in HCI +1

Designing Ground Truth and the Social Life of Labels

Ground-truth labeling is an important activity in machine learning. Many studies have examined how crowdworkers apply labels to records in machine learning datasets. However, there have been few studies that have examined the work of domain experts when their knowledge and expertise are needed to apply labels. We p…

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